DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion.
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| Title: | DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion. |
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| Authors: | Liu, Cong1 (AUTHOR), Gao, Quanwei2 (AUTHOR), Song, Chenxi1,2 (AUTHOR), Ouyang, Bo1,2 (AUTHOR), Wang, Ruyu1 (AUTHOR) wangruyu@nwafu.edu.cn, Fan, Hongtao1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1852. 29p. |
| Subjects: | Autoencoders, Object recognition algorithms, Remote sensing, Deep learning, Image reconstruction |
| Abstract: | Highlights: What are the main findings? A Differentiated Guided Reconstruction Masked Autoencoder (DGR-MAE) is proposed for cloud-occluded aircraft recognition by incorporating global attention scoring and posterior semantic correction into masked image modeling. DGR-MAE achieves the best performance among the evaluated self-supervised learning methods on the ASRAir benchmark, reaching 74.28% Top-1 accuracy without increasing inference complexity. What are the implications of the main findings? Robust semantic representation learning can improve recognition robustness under cloud-induced information loss by directly learning from incomplete observations. The findings highlight the potential of representation learning-based approaches for remote sensing target recognition under cloud occlusion. Fine-grained aircraft recognition in optical remote sensing imagery remains highly challenging under cloud occlusion, as visibility degradation causes structural information loss and weakens discriminative representation learning. To address this issue, we propose DGR-MAE, a teacher–student masked image modeling framework based on posterior semantic correction for robust representation learning under incomplete observations. Unlike existing semantic-guided masking methods that modify token visibility during input construction, DGR-MAE preserves high-ratio stochastic masking in the student branch and introduces semantic correction after visibility degradation through teacher-guided differential reconstruction. Specifically, a semantic-aware teacher branch estimates patch-level importance to partition masked regions into semantic-critical and non-critical subsets, enabling region-dependent reconstruction prioritization. A collaborative feature refinement mechanism is further incorporated to enhance contextual consistency and structural reasoning during pretraining. To support controlled evaluation, we construct the ASRAir benchmark with hierarchical cloud occlusion levels. Experimental results show that DGR-MAE achieves 74.28% Top-1 accuracy on ASRAir-Occ and achieves the best Top-1 performance while maintaining competitive Top-5 accuracy compared with representative self-supervised baselines. In particular, it demonstrates substantially improved robustness under moderate-to-severe cloud occlusion, validating the effectiveness of posterior semantic correction for remote sensing representation learning under visibility degradation. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194587073 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Cong%22">Liu, Cong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Quanwei%22">Gao, Quanwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Chenxi%22">Song, Chenxi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ouyang%2C+Bo%22">Ouyang, Bo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Ruyu%22">Wang, Ruyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangruyu@nwafu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fan%2C+Hongtao%22">Fan, Hongtao</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 11, p1852. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+algorithms%22">Object recognition algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? A Differentiated Guided Reconstruction Masked Autoencoder (DGR-MAE) is proposed for cloud-occluded aircraft recognition by incorporating global attention scoring and posterior semantic correction into masked image modeling. DGR-MAE achieves the best performance among the evaluated self-supervised learning methods on the ASRAir benchmark, reaching 74.28% Top-1 accuracy without increasing inference complexity. What are the implications of the main findings? Robust semantic representation learning can improve recognition robustness under cloud-induced information loss by directly learning from incomplete observations. The findings highlight the potential of representation learning-based approaches for remote sensing target recognition under cloud occlusion. Fine-grained aircraft recognition in optical remote sensing imagery remains highly challenging under cloud occlusion, as visibility degradation causes structural information loss and weakens discriminative representation learning. To address this issue, we propose DGR-MAE, a teacher–student masked image modeling framework based on posterior semantic correction for robust representation learning under incomplete observations. Unlike existing semantic-guided masking methods that modify token visibility during input construction, DGR-MAE preserves high-ratio stochastic masking in the student branch and introduces semantic correction after visibility degradation through teacher-guided differential reconstruction. Specifically, a semantic-aware teacher branch estimates patch-level importance to partition masked regions into semantic-critical and non-critical subsets, enabling region-dependent reconstruction prioritization. A collaborative feature refinement mechanism is further incorporated to enhance contextual consistency and structural reasoning during pretraining. To support controlled evaluation, we construct the ASRAir benchmark with hierarchical cloud occlusion levels. Experimental results show that DGR-MAE achieves 74.28% Top-1 accuracy on ASRAir-Occ and achieves the best Top-1 performance while maintaining competitive Top-5 accuracy compared with representative self-supervised baselines. In particular, it demonstrates substantially improved robustness under moderate-to-severe cloud occlusion, validating the effectiveness of posterior semantic correction for remote sensing representation learning under visibility degradation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18111852 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1852 Subjects: – SubjectFull: Autoencoders Type: general – SubjectFull: Object recognition algorithms Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Image reconstruction Type: general Titles: – TitleFull: DGR-MAE: Posterior Semantic Correction Masked Autoencoder for Fine-Grained Aircraft Recognition Under Cloud Occlusion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Cong – PersonEntity: Name: NameFull: Gao, Quanwei – PersonEntity: Name: NameFull: Song, Chenxi – PersonEntity: Name: NameFull: Ouyang, Bo – PersonEntity: Name: NameFull: Wang, Ruyu – PersonEntity: Name: NameFull: Fan, Hongtao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 11 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
